feat(seaborn): implement line-win-probability

plots/line-win-probability/implementations/seaborn.py

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+""" pyplots.ai
+line-win-probability: Win Probability Chart
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: 91/100 | Created: 2026-03-20
+"""
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Seaborn theme and context for global styling
+sns.set_theme(
+    style="ticks",
+    rc={
+        "axes.facecolor": "#F7F9FC",
+        "figure.facecolor": "#FFFFFF",
+        "grid.color": "#D0D8E0",
+        "grid.alpha": 0.3,
+        "grid.linewidth": 0.6,
+        "font.family": "sans-serif",
+    },
+)
+sns.set_context("talk", font_scale=1.05, rc={"lines.linewidth": 2.8})
+
+# Palette from seaborn
+palette = sns.color_palette(["#306998", "#D4583B"])
+home_color = palette[0]
+away_color = palette[1]
+
+# Data
+np.random.seed(42)
+
+plays = np.arange(0, 121)
+win_prob = np.full(len(plays), 0.50)
+
+events = {
+    8: ("FG Home", 0.07),
+    22: ("TD Away", -0.15),
+    35: ("TD Home", 0.18),
+    48: ("INT Home", 0.10),
+    55: ("FG Away", -0.08),
+    65: ("TD Home", 0.16),
+    78: ("TD Away", -0.14),
+    85: ("FG Home", 0.09),
+    95: ("TD Away", -0.20),
+    105: ("TD Home", 0.22),
+    115: ("FG Home", 0.08),
+}
+
+for i in range(1, len(plays)):
+    noise = np.random.normal(0, 0.015)
+    if i in events:
+        shift = events[i][1]
+    else:
+        shift = 0
+    win_prob[i] = np.clip(win_prob[i - 1] + shift + noise, 0.02, 0.98)
+
+win_prob[-1] = 0.95
+
+df = pd.DataFrame({"play": plays, "win_probability": win_prob})
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+sns.lineplot(data=df, x="play", y="win_probability", color=home_color, linewidth=2.8, ax=ax)
+
+ax.fill_between(plays, win_prob, 0.5, where=(win_prob >= 0.5), color=home_color, alpha=0.2, interpolate=True)
+ax.fill_between(plays, win_prob, 0.5, where=(win_prob < 0.5), color=away_color, alpha=0.2, interpolate=True)
+
+ax.axhline(y=0.5, color="#888888", linewidth=1.2, linestyle="--", alpha=0.5)
+
+key_events = {
+    22: ("TD Away\n7-3", away_color),
+    35: ("TD Home\n10-7", home_color),
+    65: ("TD Home\n20-14", home_color),
+    95: ("TD Away\n23-27", away_color),
+    105: ("TD Home\n30-27", home_color),
+}
+
+annotation_offsets = {22: (-8, 0.10), 35: (0, -0.10), 65: (8, 0.10), 95: (-12, 0.10), 105: (0, -0.12)}
+
+for play_num, (label, color) in key_events.items():
+    y_val = win_prob[play_num]
+    x_off, y_off = annotation_offsets[play_num]
+    ax.annotate(
+        label,
+        xy=(play_num, y_val),
+        xytext=(play_num + x_off, y_val + y_off),
+        fontsize=13,
+        fontweight="bold",
+        color=color,
+        ha="center",
+        va="center",
+        arrowprops={"arrowstyle": "->", "color": color, "lw": 1.5, "connectionstyle": "arc3,rad=0.1"},
+    )
+
+sns.scatterplot(
+    x=list(key_events),
+    y=[win_prob[p] for p in key_events],
+    color=[key_events[p][1] for p in key_events],
+    s=100,
+    zorder=5,
+    edgecolor="white",
+    linewidth=1.5,
+    ax=ax,
+    legend=False,
+)
+
+# Quarter markers
+for q, label in [(30, "Q1"), (60, "Q2"), (90, "Q3"), (120, "Q4")]:
+    ax.axvline(x=q, color="#C0C8D0", linewidth=0.8, linestyle=":", alpha=0.6)
+    ax.text(q - 15, 0.025, label, fontsize=14, color="#8899AA", ha="center", fontweight="medium")
+
+# Style
+ax.set_xlabel("Play Number", fontsize=20)
+ax.set_ylabel("Home Win Probability", fontsize=20)
+ax.set_title("line-win-probability · seaborn · pyplots.ai\n", fontsize=24, fontweight="medium", pad=4)
+ax.text(
+    0.5,
+    1.02,
+    "NFL Game — Home vs Away  |  Lead changes and momentum shifts across 120 plays",
+    transform=ax.transAxes,
+    ha="center",
+    fontsize=14,
+    color="#667788",
+    fontstyle="italic",
+)
+ax.tick_params(axis="both", labelsize=16)
+
+ax.set_ylim(0, 1)
+ax.set_xlim(0, 120)
+ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
+ax.set_yticklabels(["0%", "25%", "50%", "75%", "100%"])
+
+sns.despine(ax=ax)
+ax.yaxis.grid(True, alpha=0.2, linewidth=0.6)
+
+home_patch = mpatches.Patch(color=home_color, alpha=0.4, label="Home")
+away_patch = mpatches.Patch(color=away_color, alpha=0.4, label="Away")
+ax.legend(handles=[home_patch, away_patch], fontsize=16, loc="upper left", frameon=False)
+
+ax.text(
+    118,
+    0.015,
+    "Final: Home 30 – Away 27",
+    fontsize=15,
+    ha="right",
+    color="#556677",
+    fontweight="semibold",
+    fontstyle="italic",
+    bbox={"boxstyle": "round,pad=0.3", "facecolor": "#E8EEF4", "edgecolor": "#C0C8D0", "alpha": 0.8},
+)
+
+# Save
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-win-probability/metadata/seaborn.yaml

@@ -0,0 +1,224 @@
+library: seaborn
+specification_id: line-win-probability
+created: '2026-03-20T12:32:04Z'
+updated: '2026-03-20T12:45:51Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 23342814414
+issue: 4418
+python_version: 3.14.3
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-win-probability/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-win-probability/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent data storytelling with annotated scoring events showing running score
+  - Polished design with custom background, cohesive palette, and styled final score
+    box
+  - Complete spec compliance — all required features implemented
+  - Realistic NFL game data with compelling back-and-forth narrative
+  weaknesses:
+  - Q4 quarter label is obscured by the Final score box (minor overlap issue)
+  - Library mastery could be stronger — core visualization relies heavily on matplotlib
+    rather than seaborn-specific plotting features
+  image_description: 'The plot displays a win probability line chart for an NFL game
+    across 120 plays. A dark blue line (#306998) traces the home team''s win probability
+    from ~50% at kickoff through various scoring events. The area above the 50% dashed
+    baseline is filled with translucent blue (Home) and below with translucent salmon/red
+    (#D4583B, Away). Five key events are annotated with curved arrows and bold colored
+    text showing the scoring play and running score: "TD Away 7-3", "TD Home 10-7",
+    "TD Home 20-14", "TD Away 23-27", and "TD Home 30-27". Dotted vertical lines mark
+    Q1, Q2, and Q3 boundaries. The y-axis shows 0%-100% with the 50% reference line.
+    A styled box at the bottom right displays "Final: Home 30 – Away 27". The title
+    follows the required format. A legend in the upper left shows Home (blue) and
+    Away (red) patches. The background is a subtle light blue-gray (#F7F9FC). An italic
+    subtitle reads "NFL Game — Home vs Away | Lead changes and momentum shifts across
+    120 plays".'
+  criteria_checklist:
+    visual_quality:
+      score: 29
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 8
+        max: 8
+        passed: true
+        comment: 'All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt,
+          annotations 13pt'
+      - id: VQ-02
+        name: No Overlap
+        score: 5
+        max: 6
+        passed: true
+        comment: Q4 quarter label hidden behind Final score box; remaining annotations
+          well-spaced
+      - id: VQ-03
+        name: Element Visibility
+        score: 6
+        max: 6
+        passed: true
+        comment: Line, fills, and scatter markers all clearly visible with appropriate
+          sizing
+      - id: VQ-04
+        name: Color Accessibility
+        score: 4
+        max: 4
+        passed: true
+        comment: Blue and salmon provide strong contrast, colorblind-distinguishable
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 4
+        max: 4
+        passed: true
+        comment: Good 16:9 layout, balanced margins, nothing cut off
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: Descriptive labels with percentage units on y-axis
+    design_excellence:
+      score: 16
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
+        passed: true
+        comment: Custom background, cohesive palette, italic subtitle, styled score
+          box — clearly above defaults
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
+        passed: true
+        comment: Spines removed, subtle grid, custom background, polished quarter
+          markers
+      - id: DE-03
+        name: Data Storytelling
+        score: 5
+        max: 6
+        passed: true
+        comment: Annotations with running score, fill colors for momentum, final score
+          box — strong narrative
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: Correct line chart showing win probability over game progression
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: 'All features: 50% reference, fill areas, annotations, final score,
+          quarter markers'
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X=Play Number, Y=Win Probability, full range displayed
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Correct title format and Home/Away legend patches
+    data_quality:
+      score: 15
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 6
+        max: 6
+        passed: true
+        comment: Lead changes, momentum swings, multiple event types, both teams scoring
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: NFL game scenario, neutral sports context, plausible data
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: 120 plays realistic for NFL, probability values in valid range
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: Clean imports → data → plot → save flow, no functions or classes
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: np.random.seed(42) set
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Appropriate complexity, no fake functionality
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png with dpi=300
+    library_mastery:
+      score: 6
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
+        max: 5
+        passed: true
+        comment: Good use of seaborn axes-level API, set_theme, set_context, despine
+      - id: LM-02
+        name: Distinctive Features
+        score: 2
+        max: 5
+        passed: false
+        comment: Seaborn features are mainly styling utilities; core viz relies on
+          matplotlib
+  verdict: APPROVED
+impl_tags:
+  dependencies: []
+  techniques:
+  - annotations
+  - custom-legend
+  - manual-ticks
+  patterns:
+  - data-generation
+  - explicit-figure
+  dataprep:
+  - cumulative-sum
+  styling:
+  - alpha-blending
+  - grid-styling
+  - edge-highlighting